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Capability 03 Fine-tuned open-weight models
Fine-tune open-weight LLMs for enterprise use
Hopbyte fine-tunes open-weight models on your data and hosts them inside your own cloud, for the use cases where a general-purpose API is too expensive, too slow, or not allowed to see the data.
01 What we build
Built to run, not to demo.
A fine-tuned open-weight model is the right tool for a narrow, high-volume, or sensitive task. It is the wrong tool for general reasoning, and the wrong tool when a better prompt or retrieval would have solved the problem. Hopbyte starts every engagement with that fit check, and will say so when tuning is not justified.
Hopbyte's founder fine-tunes open-weight models for specialized use cases and runs agentic systems in production inside a large enterprise. The work covers the full path: data curation, training, evaluation, quantization, private serving, and monitoring for drift once the model is live.
Typical scopefig. 03
01
Classification and extraction
Ticket routing, document tagging, entity and field extraction from your own formats. High volume, fixed schema, measurable accuracy.
02
Domain question answering
Models that speak your vocabulary and cite your sources, hosted where the source material is allowed to live.
03
Tool-calling in a fixed schema
Small models tuned to drive a specific agent's tools reliably, at lower latency and cost than a frontier model on every step.
04
Code for your internal stack
Assistance tuned on your internal libraries, conventions, and infrastructure patterns that public models have never seen.
02 How it works
Four steps, every time.
The same path from problem to production, with evaluation and security built into each step.
Step 01
Fit check
Define the task and the metric. Baseline a frontier model and a base open-weight model. Decide whether tuning is justified by privacy, latency, cost, or specialization.
Step 02
Data
Assemble examples from your logs, documents, and past decisions. Clean, deduplicate, split, and label where needed, with a privacy review of what goes in.
Step 03
Train and evaluate
LoRA first, full fine-tune if it plateaus. A held-out evaluation set, task metrics, and a side-by-side against the baselines on quality and cost.
Step 04
Serve and govern
Private hosting in your VPC with autoscaling, access control, and logging. Drift monitoring and a retraining plan so the model does not decay quietly.
03 What you get
Deliverables you own.
- Tuned model weights delivered into your accounts, with the base model license documented
- A reproducible training pipeline with versioned data, configs, and adapters
- An evaluation report comparing the tuned model with frontier and base baselines on quality, latency, and cost
- A serving stack in your VPC with an authenticated inference API and autoscaling
- Quantized variants where they meet the quality bar at lower serving cost
- A cost model for training and inference at your expected volume
- Runbooks for retraining, rollback, and monitoring
04 Guardrails
Security and governance, designed in.
Training data never leaves your account, and its provenance is documented: where each example came from and whether it was allowed to be used. Base model licenses are reviewed against your use case before any training run. The tuned model is released only after it clears the evaluation gate and a red-team pass for prompt injection, data leakage, and refusal behavior.
The inference endpoint sits behind your identity provider, logs every request, and can be rolled back to the base model in minutes. When the model powers an agent, it inherits the same tool boundaries and approval gates Hopbyte applies to every production agent.
Every engagement ships with Data stays in your accountLicense reviewEval gatesRed-team before releaseRollback to base
05 Who this is for
A good fit when.
Audiencefig. 03b
01
Regulated and security-sensitive organizations
whose data cannot be sent to a third-party API, or who need inference to stay inside a specific region or VPC.
02
Teams with high-volume LLM workloads
where per-token API cost is now a line item and a smaller specialized model would do the job.
03
Product and platform teams
with a narrow task, a real evaluation set, and a need for model ownership rather than vendor dependence.
06 FAQ
Straight answers.
Q.01LoRA or full fine-tune?
LoRA first. It is cheaper, faster to iterate, and easy to revert because the base weights are untouched. A full fine-tune is justified when LoRA plateaus on the evaluation set or when the task changes the model's behavior deeply. The eval numbers make the call.
Q.02Do we own the model?
Yes. The weights, adapters, training data pipeline, evaluation suite, and serving code are delivered into your accounts. Hopbyte keeps nothing you would need to retrain.
Q.03Which base models do you use?
Current open-weight model families whose licenses permit your use case, chosen by the fit check and the evaluation results rather than by popularity. Hopbyte will recommend a frontier API instead when that is the honest answer.
Contact
Not sure whether to fine-tune?
Send a description of the task and the constraints. The founder will tell you whether tuning is justified, and what the cheaper alternative would be if it is not.
Start a conversation
info@hopbyte.net
Alpharetta, Georgia / Working with teams everywhere